Automatic analysis of carotid vessel wall in MR black blood images using custom convolutional trajectories

Atherosclerotic plaque is a major cause of ischemic stroke. Some arterial morphological features obtained from MR vessel wall images show great potential for identifying high-risk plaques. Deep learning has now been applied to the automatic segmentation of vessel walls to accurately and efficiently measure arterial morphological features. However, the accuracy of the existing segmentation methods is not yet high enough for clinical practical applications. This study proposed a new segmentation framework with custom convolutional trajectories for automatic segmentation of arterial vessel wall and the framework improved the accuracy of vessel wall segmentation.